Working with FastSpecFit Data

This page is for users working with existing FastSpecFit catalogs — either from a public DESI data release or produced locally. See the VACs page for links to the public catalogs, and the fastspec and fastphot data model pages for complete column descriptions and units. For worked examples, see the Tutorials page.

Reading a Catalog

The recommended way to open a FastSpecFit output file is fastspecfit.io.read_fastspecfit(), which returns the catalog tables as astropy.table.Table objects:

from fastspecfit.io import read_fastspecfit

fastfile = '/path/to/fastspec-iron-main-dark.fits'
meta, specphot, fastfit, coadd_type, fastphot = read_fastspecfit(fastfile)

The five return values are:

  • meta — targeting metadata and photometry (the METADATA extension).

  • specphot — spectrophotometric measurements such as synthesized magnitudes (the SPECPHOT extension).

  • fastfit — continuum and emission-line fitting results, or None in fastphot mode (the FASTSPEC extension).

  • coadd_type — coadd type string from the file header (e.g., healpix).

  • fastphot — True if the file was produced in photometry-only mode.

The two tables are row-matched.

Note

The merged FastSpecFit catalogs distributed as part of the DESI VACs do not include the MODELS extension in order to keep file sizes manageable. Model spectra are available in the per-healpix output files produced during a production run. To load them:

meta, specphot, fastfit, coadd_type, fastphot, models = \
    read_fastspecfit(fastfile, read_models=True)

The models array has shape (N, 3, Nwave), where the three components are the stellar continuum, the smooth continuum correction, and the emission-line model. The corresponding wavelength array is encoded in the MODELS extension header:

import numpy as np, fitsio
models, hdr = fitsio.read(fastfile, 'MODELS', header=True)
modelwave = hdr['CRVAL1'] + np.arange(hdr['NAXIS1']) * hdr['CDELT1']

Accessing Key Quantities

Both fastfit and meta are standard astropy.table.Table objects that support column access, boolean masking, and cross-matching. A few commonly used quantities:

import numpy as np
from fastspecfit.io import read_fastspecfit

meta, specphot, fastfit, coadd_type, fastphot = read_fastspecfit(fastfile)

# Redshifts and stellar masses
redshift = fastfit['Z']
logmstar  = fastfit['LOGMSTAR']

# H-alpha flux and signal-to-noise
halpha_flux = fastfit['HALPHA_FLUX']
halpha_snr  = halpha_flux * np.sqrt(fastfit['HALPHA_FLUX_IVAR'])

# Select star-forming galaxies with significant H-alpha detections
sf = (halpha_snr > 3) & (logmstar > 9)
print(f'{sf.sum()} star-forming galaxies selected')

See the fastspec data model for a complete description of every column.